Semantic segmentation-based traffic sign detection and recognition using deep learning techniques

Abstract

We present a method for detecting and
classifying traffic signs based on two deep neural
network architectures. A Fully Convolutional Network
(FCN) – based semantic segmentation model is
modified to extract traffic sign regions of interest.
These regions are further passed to a Convolutional
Neural Network (CNN) for traffic sign classification.
We propose a novel CNN architecture for the
classification step. In evaluating our approach, we
contrast the efficiency and the robustness of the deep
learning image segmentation approach with classical
image processing filters traditionally applied for traffic
sign detection. We also show the effectiveness of our
CNN-based recognition method by integrating it in our
system.